Online Learning for Deterministic Dependency Parsing

Deterministic parsing has emerged as an effective alternative for complex parsing algorithms which search the entire search space to get the best probable parse tree. In this paper, we present an online large margin based training framework for deterministic parsing using Nivre’s Shift-Reduce parsing algorithm. Online training facilitates the use of high dimensional features without creating memory bottlenecks unlike the popular SVMs. We participated in the CoNLL Shared Task-2007 and evaluated our system for ten languages. We got an average multilingual labeled attachment score of 74.54 % (with 65.50% being the average and 80.32% the highest) and an average multilingual unlabeled attachment score of 80.30% (with

• Type: Conference
• Year: 2007
• Where: EMNLP
• Authors: Prashanth Mannem
• Keywords: Deterministic Parsing, EMNLP 2007, Natural Language Processing, Parsing Algorithm, Shift-Reduce Parsing Algorithm